def download_command(
dataset: str = typer.Argument("sdo-benchmark"),
destination: Path = typer.Argument(Path("./")),
verbose: bool = typer.Option(False, "--verbose", "-v"),
):
if verbose:
set_log_level(logging.INFO)
download_dataset(dataset, destination)
def upload_command(
path: Path,
name: str,
verbose: bool = typer.Option(False, "--verbose", "-v"),
):
if verbose:
set_log_level(logging.INFO)
upload_model(path, name)
def dataset_command(
config_file: Path = typer.Argument(Path("config") / "dataset.yaml"),
verbose: bool = typer.Option(False, "--verbose", "-v"),
):
if verbose:
set_log_level(logging.INFO)
params = load_yaml(config_file)
make_dataset(params)
def test_command(
parameters_overrides: Optional[List[str]] = typer.Argument(None),
verbose: bool = typer.Option(False, "--verbose", "-v"),
):
if verbose:
set_log_level(logging.INFO)
config = read_config(parameters_overrides)
logger.info(f"Params: {config}")
test(config, verbose)
def data_stats_command(
split: Split = Split.train,
n_bins: int = 100,
hist_path: Optional[Path] = None,
transform: bool = typer.Option(False, "--transform", "-t"),
parameters_overrides: Optional[List[str]] = typer.Argument(None),
verbose: bool = typer.Option(False, "--verbose", "-v"),
):
if verbose:
set_log_level(logging.INFO)
config = read_config(parameters_overrides)
logger.info(f"Params: {config}")
stats_dataset(config, split.value, n_bins, hist_path, transform)
def dag_graph_command(
input: Path,
verbose: bool = typer.Option(False, "--verbose", "-v"),
):
if verbose:
set_log_level(logging.INFO)
with open(input, "r", encoding="utf-8") as file:
print("```mermaid\ngraph TD;")
for line in file:
if "->" in line:
parts = line.strip().replace('"',
"").replace("@", ":").replace(
";", "").split(" ")
print(f"{parts[2]} --> {parts[0]}")
print("```")
def train_command(
parameters_overrides: Optional[List[str]] = typer.Argument(None),
verbose: bool = typer.Option(False, "--verbose", "-v"),
):
if verbose:
set_log_level(logging.INFO)
config = read_config(parameters_overrides)
logger.info(f"Params: {config}")
training_type = config["training_type"]
if training_type == "train":
train_standard(config)
elif training_type == "ssl":
train_ssl(config)
elif training_type == "finetune":
finetune(config)
def main(verbose: bool = typer.Option(False, "--verbose", "-v")):
if verbose:
set_log_level(logging.INFO)
import typer
from omegaconf import OmegaConf
from solarnet.tasks.dataset import make_dataset
from solarnet.tasks.download_dataset import download_dataset
from solarnet.tasks.stats_dataset import stats_dataset
from solarnet.tasks.upload import upload_model
from solarnet.utils.log import init_log, set_log_level
from solarnet.utils.yaml import load_yaml
init_log()
from solarnet.tasks.test import test
from solarnet.tasks.train import train_standard, train_ssl, finetune
set_log_level(logging.WARNING)
logger = logging.getLogger()
app = typer.Typer(context_settings={"help_option_names": ["-h", "--help"]})
os.environ["PL_TORCH_DISTRIBUTED_BACKEND"] = "gloo"
def read_config(parameters_overrides: Optional[List[str]]) -> dict:
# Initialize configuration (using Hydra)
hydra.initialize(config_path="./../config")
config = hydra.compose(config_name="config",
overrides=parameters_overrides)
return OmegaConf.to_container(config, resolve=True)
@app.command("train")
def train_command(